Call Center Staffing Models
|
|
- Hortense Malone
- 3 years ago
- Views:
Transcription
1 Proceedings of the 2008 Winter Simulation Conference S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A SIMULATION BASED SCHEDULING MODEL FOR CALL CENTERS WITH UNCERTAIN ARRIVAL RATES Thomas R. Robbins Dept. of Marketing and Supply Chain East Carolina University Greenville, NC 27858, U.S.A Terry P. Harrison Dept. of Supply Chain and Information Systems Penn State University University Park, PA 16802, U.S.A. ABSTRACT In this paper we develop a two stage algorithm for scheduling call centers with strict SLAs and arrival rate uncertainty. The first cut schedule can be developed in less than a minute using a constructive heuristic. The schedule is then refined via a simulation based optimization approach. We find that when allowed to run for five minutes or less this two stage process can create a schedule with a total expected cost within a few percentage points of schedules generated using much more computationally intensive methods. This rapid scheduling process is designed to support front line managers who wish to evaluate multiple scheduling options in a what if analysis mode. 1 INTRODUCTION Call centers are a large and growing component of the U.S. and world economy (Gans, Koole et al. 2003; Aksin, Armony et al. 2007) The Incoming Call Management Institute estimates that by 2008 the US will have over 47,000 call centers and employ over 2.7 million agents. Large scale call centers are technically and managerially sophisticated operations and have been the subject of substantial academic research. A call center is a facility designed to support the delivery of some interactive service via telephone communications, typically an office space with multiple workstations manned by agents who place and receive calls. Call center applications include telemarketing, customer service, help desk support, and emergency dispatch. Inbound call center operations are challenging to manage; there is considerable uncertainty in estimates of arrival rates, and the operation is often subject to strict service level constraints. Our research is motivated in part by recent work with a medium sized provider of call center based technical support. While the scope of services varies from account to account, many projects are 24 x 7 support and virtually all are subject to some form of Service Level Agreement (SLA). There are multiple types of SLAs, but the most common specifies a minimum level of the Telephone Service Factor (TSF). A TSF SLA specifies the proportion of calls that must be answered within a specified time. For example, an 80/120 SLA specifies that 80% of calls must be answered within 120 seconds. A very important point is that the service level applies to an extended period, typically a week or a month, which appears to be common practice in this segment of the industry. The SLA does not define requirements for a day or an hour; so the desk is typically staffed so that at some times the service level is underachieved, sometimes overachieved, and is on target for the entire month. The outsourcing contract often specifies substantial financial penalties for failing to meet the SLA. The key difficulty involved with staffing this call center is a fixed SLA with a variable and uncertain arrival rate pattern. The number of calls presented in any ½ hour period is highly variable with multiple sources of uncertainty. In addition to day of week seasonality these call center projects also experience very significant time of day seasonality. The staffing challenge in this call center is to find a minimal cost staffing plan that achieves the global SLA target with a high probability. The schedule must obviously be locked in before arrival rate uncertainty is revealed. While management has some recourse to adjust manpower during the course of the day (overtime, early dismissal) these actions are generally very limited. Standard call center scheduling models typically enforce the service level in every time period and therefore often over staff the call center. Most models also fail to adequately address the uncertainty in inbound call volume and tend to under staff in volatile periods. Managers often manually adjust the schedules based on experience to compensate for these shortcomings, making extensive optimization moot. Several recent scheduling models attempt to address this uncertainty, but result in computationally intensive algorithms. We develop an algorithm that can develop near optimal schedules very quickly to support interactive planning /08/$ IEEE 2884
2 2 LITERATURE REVIEW Detailed summaries of the call center oriented literature are provided in (Gans, Koole et al. 2003) and (Aksin, Armony et al. 2007). Many call center scheduling models are derivatives of the set covering model developed in (Dantzig 1954). Dantzig s model assumed by period staffing requirements were defined exogenously, as were a set of feasible schedules. His algorithm seeks the lowest cost covering of the requirements. The set covering model is often formulated as a Mixed Integer Program and becomes intractable as the number of schedules becomes large, which is common if operations are continuous and breaks are scheduled explicitly. Many models implement a two phased approach where breaks are ignored in the first phase and are then scheduled in a second pass. This general approach is outlined in (Pinedo 1995) and examined in more detail in (Thompson 1995; Aykin 1996; Brusco and Jacobs 2000). More recently attention has been focused on the issue of uncertainty in arrival rates. Statistical analysis of call center data in (Brown, Gans et al. 2005; Robbins 2007) supports the notion that arrival rates are uncertain. (Bassamboo, Harrison et al. 2005) develop a model that attempts to minimize the cost of staffing plus an imputed cost for customer abandonment for a call center with multiple customer and server types when arrival rates are variable and uncertain. (Harrison and Zeevi 2005) solve the staffing problem for call centers with multiple call types, multiple agent types, and uncertain arrivals using a fluid approximation. (Atlason, Epelman et al. 2007) develop an algorithm that combines server sizing and staff scheduling into a single optimization problem. This model focuses on the impact that staffing in one time period can affect performance in the subsequent period, a fact ignored in many models. The algorithm utilizes discrete event simulation to calculate service levels under candidate staffing models, and a discrete cutting plane algorithm to search for improving solutions. This approach has the advantage of making accurate service level calculations for call centers with nonstationary arrival patterns, but this comes at a high computational cost. (Robbins and Harrison 2008) develop a modified version of the set covering approach that works to a globally optimum schedule when arrival rates are uncertain. This model is formulated a stochastic mixed integer program and requires significant computational effort to solve. 3 SCHEDULING MODEL The objective of this paper is to develop a scheduling model that can operate in an interactive decision support framework. As such it should allow for very rapid development of good schedules to support interactive planning in a what-if approach. It should then allow schedules to be refined and optimized. Finally it should operate on a standard desktop computer to allow front line supervisors to run it interactively. To accomplish this objective we developed a two stage process. In the first stage we use a constructive heuristic to incrementally develop a preliminary schedule that will satisfy the aggregate service level requirements based on a stationary period by period approximation of the service level. In the second stage we evaluate the schedule via discrete event simulation and use a local search algorithm to find lower cost schedules. 3.1 Constructive Heuristic The constructive heuristic builds a schedule using a greedy algorithm; it iteratively adds assignments that cover the highest expected number of calls answered outside of the service level requirement. To define the algorithm more formally we use the following notation. Sets I: time periods J: possible schedules K: scenarios Deterministic Parameters a ij : a 1 if schedule j is staffed in time i, 0 otherwise g: global SLA goal d: allowable delay before a call must be answered μ: minimum number of agents in any time period r: per point penalty cost of TSF shortfall Decision Variables x j : number of resources assigned to schedule j State Variables S i : Average TSF shortfall in period i b j : shortfall calls covered by schedule j C j : Cover for schedule j A: the expected aggregate service level O: the service level offset Stochastic Parameters n ik : number of calls in period i of scenario k Each scenario in the set K represents a vector of call volumes per time period in the planning horizon. These call volumes can be generated using any approach that can characterize the variability of the call stream. In our approach we assume that the call volume on any given day is a normally distributed random variable. We further assume that the proportion of calls received during any 30 minute period is a normally distributed random variable. We assume the parameters for each 30 minute period are consistent across all days of the weeks. The call generation algorithm is detailed in (Robbins 2007). 2885
3 The average shortfall in each time period is calculated by: nik 1 TSF nik, aijxj, d k K j J S i = (1) K The function TSF(, v n, d) calculates the telephone service factor, the proportion of calls answered within d seconds, if call volume is v, and the number of agents is n. This calculation is based on the Erlang A model and implemented using the fluid approximations defined in (Garnett, Mandelbaum et al. 2002). The measure S i is the average number of calls that are not answered within the allowable delay for time period i. Periods with a large number of out of service level calls are good candidates for incremental staffing. To determine where to best add incremental staffing we calculate the cover for each feasible schedule. The cover is defined as the average number of out of service level calls presented to this schedule per period. Sa i ij i I C j = (2) a i I The next schedule to staff is selected as the schedule with the maximum cover. Based on these definitions we can outline a basic initial staffing heuristic as follows. 1. Initialization a. Generate a set of K call volume scenarios b. Create an initial schedule with no staffing c. Define a target service level scheduling bias 2. Repeat the following until Stop=True a. Find the schedule in J with maximum cover C j and increment staffing for that schedule. b. Calculate the per period out of service level call volume S i c. Calculate A, the aggregate expected service level, i.e. the average proportion of calls answered with d seconds. d. If A g + O then Stop = True Figure 3-1 Staffing Heuristic The algorithm continues until the estimated aggregate service level reaches the goal level plus an offset. The offset parameter is a factor that allows the stopping point to be biased relative to the goal. Our empirical results show that better schedule can be found by applying a small offset that varies based on the seasonality of the project. The offset allows for an adjustment to compensate for any bias in the stationary period by period based service level approximation. ij The Minimum Staffing Problem The algorithm outlined in Figure 3-1 can quickly generate a schedule that will satisfy the aggregate service level requirement, however the resulting schedule may not meet minimum staffing requirements. A minimum staffing requirement necessitates a minimum number of agents that must be scheduled at all times. In the operation we analyzed a minimum of two agents are required at all times. Because operations are 24 7 and overnight call volume tends to be quite low on some projects, the staffing heuristic will often leave portions of the overnight period with no staffing, a solution that is clearly unacceptable. To address the minimum staffing issue we modified the cover calculation to add a bonus for covering time periods with staffing levels below the minimum. In practice, setting this bonus parameter turned out to be very important in terms of the quality of the final schedule generated. If the parameter was set too high, off peak hours are scheduled too early in the process and the resulting schedule is too costly. If set too low then the schedule will reach the aggregate service level goal without satisfying the minimum staffing constraint. After extensive experimentation we settled on a modified algorithm defined as follows. 1. Initialization a. Set the min staffing cover coefficient to an initial value b. Set Stop = False 2. Search: repeat until Stop=True a. Execute the staffing algorithm outline in Figure 3-1. b. If all min staffing constraints are satisfied the Stop = True, else increment the min staffing cover coefficient Figure 3-2 Modified Staffing Heuristic 3.2 Simulation Based Search In this stage we use Discreet Event Simulation (DES) to model the operation of the call center. Unlike the analytically based model used so far, the DES approach simulates individual call processing. The simulation model is designed to generate call arrivals using the same statistical model used to generate call scenarios for the initial heuristic. The simulation model also varies the number of agents based on the time phased staffing model generated from the scheduling model. Basic DES can evaluate the expected outcome of the candidate schedule, but in order to find a better schedule, we need to implement some form of optimization algorithm. We implement a local search algorithm that starts with the schedule generated from the heuristic and searches the neighborhood of closely related schedules. The local search algorithm is guided by a variable neighborhood 2886
4 search (VNS) metaheuristic. VNS is a metaheuristic that makes systematic changes in the neighborhood being searched as the search progresses (Hansen and Mladenovic 2001; Hansen and Mladenovic 2005). The general structure of the VNS is as follows: 1. Initialization a. Select the set of neighborhood structures Nk, for k = 1,..., kmax b. Construct an initial incumbent solution, x I, using a heuristic procedure. c. Select a confidence level α for the selection of a new incumbent solution 2. Search: repeat the following until Stop=True a. Set k = 1 b. Find nk min candidate solutions, x C that are neighbors of x I c. Simulate the system with each candidate and compare the results to the incumbent using a pair wise t Test at the α level of significance. d. If any xc is superior to x I at the α level then set * * xi = xc, where xc is the best candidate solution e. Else, set i = n kmin, set Found = False, and repeat until ( i = n kmax or Found=True) i. Find a new candidate x ii. Simulate the system with each candidate and compare the results using a pairwise T Test. iii. If xk i is superior to x I at the α level then set xi = xk i and found = True f. If a no new incumbent was found in neighborhood k then i. set k = k+ 1 ii. if k > kmax then Stop = True A common approach in VNS is to define a series of nested neighborhood structures such that N1( x) N2( x)... Nk Max ( x) x X When defining the neighborhood structure we make the distinction between the set of active schedules, those schedules with a non-zero assignment in the candidate schedule, and feasible schedules which include all schedules in the feasible schedule set. Based on this distinction we define the following neighborhoods N ( ) 1 x : Active 1 Change: the set of all staff plans where an active schedule is either incremented or decremented by one. k i N ( ) 2 x : Active 2 Change: pick any two active schedules and independently increment or decrement each. N ( ) 3 x : Feasible 1 Change: pick any feasible schedule and add an assignment. N ( ) 4 x : Feasible 2 Change: pick any feasible schedule and add an assignment, pick an active schedule and decrement the number assigned. 4 NUMERICAL EVALUATION To test our algorithm we evaluate it against models developed for three real world IT support projects. We refer to these projects as Project J, Project S and Project O. Each project has unique characteristics that create different seasonality patterns and different levels of variability. Project J is an IT help desk supporting approximately 30,000 corporate used. This help desk receives about 16,000 calls per month and exhibits a strong weekly and daily seasonality pattern. Call volume exhibits a strong spike in the morning between 8 and 11 AM and a smaller peak in the afternoon between 1 and 3 PM. Project O supports a corporate and store employees for a large retail chain. Call volume is slightly lower, at about 15,000 calls per month and exhibits a less dramatic daily seasonality pattern. Call volume peaks around 8AM and declines steadily throughout the day, without the narrow volume peak exhibited in Project J. Project S also supports retail and corporate users and has a seasonality pattern similar to Project O. Call volume is much higher, at about 45,000 calls per month and is much more volatile than project O. More detailed descriptions of these projects, and the parameters of the call volume models can be found in (Robbins 2007). For each project we develop schedules for five variability options; these options allow for various levels of part-time staffing. The least flexible option, A, allows only for five day a week eight hour shifts. With these restrictions there are 336 unique schedules to which agents can be assigned. The most flexible option, E, allows five day a week four hour shifts and expands the total number of feasible schedules to 3,696. The scheduling options are summarized in Table 1. Table 1: Scheduling Patterns Pattern Schedule Types Included Feasible Schedules A 5x8 only 336 B 5x8, 4x10 1,680 C 5x8, 4x10, 4x8 3,024 D 5x8, 4x10, 4x8, 5x6 3,360 E 5x8, 4x10, 4x8, 5x6, 5x4 3,
5 We evaluated each of the three project against the five schedule options for a total of 15 test cases. In each case we benchmarked our solution against the solution generated using the mixed integer stochastic program analyzed in (Robbins 2007). That algorithm generates an optimal solution based on an analytical approximation of service levels using an independent period by period approximation, but the resulting solution may not be optimal given interactions between periods and errors due to the analytic approximation of the service level. Solution times for this algorithm range from 45 to 120 minutes. Table 2 summarizes the results. Table 2: Objective Value Comparison Heuristic Only 5 Min Simulation Search Simulated Total Cost Full Simulation Search Stochastic Prog. Solution 5 Min Gap Full Search Gap Project J Sched A 12,806 12,156 12,126 12, % -0.34% Sched B 12,069 11,825 11,759 12, % -3.28% Sched C 12,373 12,088 11,923 11, % 1.27% Sched D 12,193 11,813 11,709 11, % 0.79% Sched E 12,011 11,930 11,930 11, % 4.13% Project S Sched A 33,652 31,544 30,142 31, % -3.40% Sched B 34,139 30,510 29,235 30, % -4.67% Sched C 33,856 30,472 29,283 30, % -5.29% Sched D 33,448 31,347 29,150 30, % -3.04% Sched E 33,800 32,279 29,221 30, % -3.93% Project O Sched A 13,004 12,539 12,216 12, % 0.62% Sched B 13,026 12,498 12,019 12, % -1.60% Sched C 12,990 12,441 12,193 12, % 0.45% Sched D 12,805 12,193 12,071 12, % 0.50% Sched E 12,858 12,341 12,140 11, % 1.38% We tuned the algorithm to optimize the schedule that was generated after the staffing heuristic had run and the simulation search had run for a few minutes. Testing revealed that this was best accomplished by setting the offset used in the algorithm of Figure 3-1to 5%. A smaller bias improves the quality of the solution found in the heuristic, but makes the simulation search more difficult. We found that the simulation search finds improvements solution faster when it is offset because improving single changes are easier to find than improving two changes. Overall we found that our algorithm can find solutions that evaluate within a few points of solution found by the stochastic integer program within the five minute target time frame, in some cases better than the solution found by the stochastic program. (Recall that since the stochastic program uses an analytical approximation to estimate the service level, it s solution is not necessarily optimal.) In the worst case the five minute solution is 6% more expensive than the stochastic programming solution; in the best case it is 2.7% less expensive. If allowed to run until it reaches its heuristic stopping rule, it often finds a better solution than the stochastic program, and in all but one case finds a solution within 1.5%. This verifies our hope that this algorithm can be used to find a good solution fast and if allowed to run for a longer period of time can find a near optimal solution. This supports the goal of interactive what-if analysis of schedule options followed by a more detailed search for a final schedule. Figures 1-3 show how the objective value of the incumbent solution evolves for each project for the B scheduling option. (Schedule option B in general had the most iterations.) 12,500 12,000 11,500 11,000 10,500 10,000 36,000 34,000 32,000 30,000 28,000 26,000 24,000 22,000 20,000 13,200 13,000 12,800 12,600 12,400 12,200 12,000 11,800 Objective- Total Cost (Project J/Sched B) Elapsed Minutes Figure 1: Objective Project J/Schedule B Objective- Total Cost (Project S/Sched B) Elapsed Minutes Figure 2: Objective Project S/Schedule B Objective- Total Cost (Project O/Sched B) Elapsed Minutes 2888
6 Figure 3: Objective Project O/Schedule B In each case the heuristic finds a reasonable solution that is quickly improved by the simulation based search. The degree and frequency of the improvement then drops off rapidly. In the case of Project J and Project O the algorithm terminates relatively quickly with an objective near the objective of the stochastic integer program. In the case of Project S the algorithm runs for an extended period of time, but always finds a better solution than the stochastic integer program. We believe this is due to the fact that call volume drops off slowly in Project S so there are many schedules that are near optimal. That relatively higher peaks in Projects J and O create a smaller set of near optimal solutions. 5 SUMMARY AND CONCLUSIONS In this paper we outline an algorithm and preliminary results for a call center scheduling model that can be run by a front line manager on a desktop computer. We seek an algorithm that allows the manager to quickly evaluate different scheduling options and find that in relatively short periods of time we can generate quality schedules. Because our algorithm is based on simulation and a search heuristic we can make no claims about optimality, but comparing the results to a optimal analytical approximation shows favorable results. More detailed and more structured testing of our algorithm is required to better understand the tradeoffs made when setting key tuning parameters such as the initial heuristic offset. 6 REFERENCES Aksin, Z., M. Armony and V. Mehrotra The Modern Call Center: A Multi-Disciplinary Perspective on Operations Management Research. Production and Operations Management 16(6) p Atlason, J., M. A. Epelman and S. G. Henderson Optimizing Call Center Staffing Using Simulation and Analytic Center Cutting-Plane Methods. Management Science p. 15. Aykin, T Optimal Shift Scheduling with Multiple Break Windows. Management Science 42(4) p Bassamboo, A., J. M. Harrison and A. Zeevi Design and Control of a Large Call Center: Asymptotic Analysis of an LP-based Method. Working Paper 51p. Brown, L., N. Gans, A. Mandelbaum, A. Sakov, S. Haipeng, S. Zeltyn and L. Zhao Statistical Analysis of a Telephone Call Center: A Queueing- Science Perspective. Journal of the American Statistical Association 100(469) p Brusco, M. J. and L. W. Jacobs Optimal Models for Meal-Break and Start-Time Flexibility in Continuous Tour Scheduling. Management Science 46(12) p Dantzig, G. B A Comment on Edie's "Traffic Delays at Toll Booths". Journal of the Operations Research Society of America 2(3) p Gans, N., G. Koole and A. Mandelbaum Telephone call centers: Tutorial, review, and research prospects. Manufacturing & Service Operations Management 5(2) p Garnett, O., A. Mandelbaum and M. I. Reiman Designing a Call Center with impatient customers. Manufacturing & Service Operations Management 4(3) p Hansen, P. and N. Mladenovic Variable neighborhood search: Principles and applications. European Journal of Operational Research 130(3) p Hansen, P. and N. Mladenovic (2005). Variable Neighborhood Search. Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. E. K. Burke and G. Kendall. New York, NY, Springer: Harrison, J. M. and A. Zeevi A Method for Staffing Large Call Centers Based on Stochastic Fluid Models. Manufacturing & Service Operations Management 7(1) p Pinedo, M Scheduling: theory, algorithms, and systems, Prentice Hall. Englewood Cliffs, N.J. Robbins, T. R Managing Service Capacity Under Uncertainty - Unpublished PhD Dissertation ( t/r/trr147). Working Paper 241p. Robbins, T. R. and T. P. Harrison A Stochastic Programming Model for Scheduling Call Centers with Global Service Level Agreements. Working Paper 34p. Thompson, G. M Improved Implicit Optimal Modeling of the Labor Shift Scheduling Problem. Management Science 41(4) p AUTHOR BIOGRAPHIES THOMAS ROBBINS recently earned a PhD in Business Administration and Operations Research from Penn State University. Prior to beginning his academic career he worked in professional services for approximately 18 years. He holds an MBA from Case Western Reserve and a BSEE from Penn State. He currently teaches statistics and operations management at Penn State. He is currently an Assistant Professor at East Carolina University. His address is <robbinst@ecu.edu>. TERRY P. HARRISON is the Earl P. Strong Executive Education Professor of Business and Professor of Supply 2889
7 Chain and Information Systems at Penn State University. He holds a Ph.D. and M.S. degree in Management Science from the University of Tennessee and a B.S in Forest Science from Penn State. He was formally the Editor-in- Chief of Interfaces and is currently Vice President of Publications for INFORMS. His mail address is <tharrison@psu.edu>. Robbins and Harrison 2890
Optimal Cross Training in Call Centers with Uncertain Arrivals and Global Service Level Agreements
Optimal Cross Training in Call Centers with Uncertain Arrivals and Global Service Level Agreements Working Paper Draft Thomas R. Robbins D. J. Medeiros Terry P. Harrison Pennsylvania State University,
More informationPARTIAL CROSS TRAINING IN CALL CENTERS WITH UNCERTAIN ARRIVALS AND GLOBAL SERVICE LEVEL AGREEMENTS. D. J. Medeiros
Proceedings of the 07 Winter Simulation Conference S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds. PARTIAL CROSS TRAINING IN CALL CENTERS WITH UNCERTAIN ARRIVALS
More informationAddressing Arrival Rate Uncertainty in Call Center Workforce Management
1 Addressing Arrival Rate Uncertainty in Call Center Workforce Management Thomas R. Robbins Penn State University Abstract Workforce management is a critical component of call center operations. Since
More informationA Stochastic Programming Model for Scheduling Call Centers with Global Service Level Agreements
A Stochastic Programming Model for Scheduling Call Centers with Global Service Level Agreements Working Paper Thomas R. Robbins Terry P. Harrison Department of Supply Chain and Information Systems, Smeal
More informationProceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.
Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds. DOES THE ERLANG C MODEL FIT IN REAL CALL CENTERS? Thomas R. Robbins D. J. Medeiros
More informationCapacity Management in Call Centers
Capacity Management in Call Centers Basic Models and Links to Current Research from a review article authored with Ger Koole and Avishai Mandelbaum Outline: Tutorial background on how calls are handled
More informationAppendix: Simple Methods for Shift Scheduling in Multi-Skill Call Centers
MSOM.1070.0172 Appendix: Simple Methods for Shift Scheduling in Multi-Skill Call Centers In Bhulai et al. (2006) we presented a method for computing optimal schedules, separately, after the optimal staffing
More informationNew Project Staffing for Outsourced Call Centers with Global Service Level Agreements
New Project Staffing for Outsourced Call Centers with Global Service Level Agreements Working Paper Thomas R. Robbins Terry P. Harrison Department of Supply Chain and Information Systems, Smeal College
More informationNew Project Staffing for Outsourced Call Centers with Global Service Level Agreements
New Project Staffing for Outsourced Call Centers with Global Service Level Agreements W Thomas R. Robbins Department of Marketing and Supply Chain, College of Business East Carolina University, Greenville,
More informationFlexible Workforce Management System for Call Center: A case study of public sector
Asia Pacific Management Review (2007) 12(6), 338-346 Flexible Workforce Management System for Call Center: A case study of public sector Jun Woo Kim a, Sang Chan Park a,* a Department of Industrial Engineering,
More informationMANAGING SERVICE CAPACITY UNDER UNCERTAINTY
The Pennsylvania State University The Graduate School The Mary Jean and Frank P. Smeal College of Business MANAGING SERVICE CAPACITY UNDER UNCERTAINTY A Thesis in Business Administration and Operations
More informationThis paper introduces a new method for shift scheduling in multiskill call centers. The method consists of
MANUFACTURING & SERVICE OPERATIONS MANAGEMENT Vol. 10, No. 3, Summer 2008, pp. 411 420 issn 1523-4614 eissn 1526-5498 08 1003 0411 informs doi 10.1287/msom.1070.0172 2008 INFORMS Simple Methods for Shift
More informationUSING SIMULATION TO EVALUATE CALL FORECASTING ALGORITHMS FOR INBOUND CALL CENTER. Guilherme Steinmann Paulo José de Freitas Filho
Proceedings of the 2013 Winter Simulation Conference R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl, eds USING SIMULATION TO EVALUATE CALL FORECASTING ALGORITHMS FOR INBOUND CALL CENTER Guilherme
More informationA NEW POLICY FOR THE SERVICE REQUEST ASSIGNMENT PROBLEM WITH MULTIPLE SEVERITY LEVEL, DUE DATE AND SLA PENALTY SERVICE REQUESTS
Proceedings of the 2008 Winter Simulation Conference S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A NEW POLICY FOR THE SERVICE REQUEST ASSIGNMENT PROBLEM WITH MULTIPLE SEVERITY
More information----------------------------------------------------------------------------------------------------------------------------
Ditila Ekmekçiu: Optimizing a call center performance using queueing models an Albanian case Optimizing a call center performance using queueing models an Albanian Case Ditila Ekmekçiu University of Tirana,
More informationDistributionally robust workforce scheduling in call centers with uncertain arrival rates
Distributionally robust workforce scheduling in call centers with uncertain arrival rates S. Liao 1, C. van Delft 2, J.-P. Vial 3,4 1 Ecole Centrale, Paris, France 2 HEC. Paris, France 3 Prof. Emeritus,
More informationRobust and data-driven approaches to call centers
Robust and data-driven approaches to call centers Dimitris Bertsimas Xuan Vinh Doan November 2008 Abstract We propose both robust and data-driven approaches to a fluid model of call centers that incorporates
More informationUSING SIMULATION TO PREDICT MARKET BEHAVIOR FOR OUTBOUND CALL CENTERS. 88040-900 Florianópolis SC Brazil 88040-900 Florianópolis SC Brazil
Proceedings of the 7 Winter Simulation Conference S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds. USING SIMULATION TO PREDICT MARKET BEHAVIOR FOR OUTBOUND CALL CENTERS
More informationSimple Methods for Shift Scheduling in Multi-Skill Call Centers
Simple Methods for Shift Scheduling in Multi-Skill Call Centers Sandjai Bhulai, Ger Koole & Auke Pot Vrije Universiteit, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands Final version Abstract This
More informationA STAFFING ALGORITHM FOR CALL CENTERS WITH SKILL-BASED ROUTING
A STAFFING ALGORITHM FOR CALL CENTERS WITH SKILL-BASED ROUTING by Rodney B. Wallace IBM and The George Washington University rodney.wallace@us.ibm.com Ward Whitt Columbia University ward.whitt@columbia.edu
More informationSupplement to Call Centers with Delay Information: Models and Insights
Supplement to Call Centers with Delay Information: Models and Insights Oualid Jouini 1 Zeynep Akşin 2 Yves Dallery 1 1 Laboratoire Genie Industriel, Ecole Centrale Paris, Grande Voie des Vignes, 92290
More informationAS-D1 SIMULATION: A KEY TO CALL CENTER MANAGEMENT. Rupesh Chokshi Project Manager
AS-D1 SIMULATION: A KEY TO CALL CENTER MANAGEMENT Rupesh Chokshi Project Manager AT&T Laboratories Room 3J-325 101 Crawfords Corner Road Holmdel, NJ 07733, U.S.A. Phone: 732-332-5118 Fax: 732-949-9112
More informationINTEGRATED OPTIMIZATION OF SAFETY STOCK
INTEGRATED OPTIMIZATION OF SAFETY STOCK AND TRANSPORTATION CAPACITY Horst Tempelmeier Department of Production Management University of Cologne Albertus-Magnus-Platz D-50932 Koeln, Germany http://www.spw.uni-koeln.de/
More informationResponse of an Agent-Based Emergency Call Centre Model to a Sudden Increase in Calls
The 7th International Conference on Information Technology and Applications (ICITA 211) Response of an Agent-Based Emergency Call Centre Model to a Sudden Increase in Calls Bruce G. Lewis and Ric. D. Herbert
More informationIn this paper we examine some assumptions commonly made in modeling call centers. In particular, we evaluate the
SERVICE SCIENCE Vol. 7, No. 2, June 215, pp. 132 148 ISSN 2164-3962 (print) ISSN 2164-397 (online) http://dx.doi.org/1.1287/serv.215.11 215 INFORMS Experience-Based Routing in Call Center Environments
More informationSTRATEGIC CAPACITY PLANNING USING STOCK CONTROL MODEL
Session 6. Applications of Mathematical Methods to Logistics and Business Proceedings of the 9th International Conference Reliability and Statistics in Transportation and Communication (RelStat 09), 21
More informationSHIFT-SCHEDULING OF CALL CENTERS WITH UNCERTAIN ARRIVAL PARAMETERS
8 th International Conference of Modeling and Simulation - MOSIM 1 - May 1-12, 21 - Hammamet - Tunisia Evaluation and optimization of innovative production systems of goods and services SHIFT-SCHEDULING
More informationModeling and Optimization Problems in Contact Centers
Modeling and Optimization Problems in Contact Centers Pierre L Ecuyer Département d Informatique et de Recherche Opérationnelle Université de Montréal, C.P. 6128, Succ. Centre-Ville Montréal, H3C 3J7,
More informationNear-Optimal Nonmyopic Contact Center Planning Using Dual Decomposition
Near-Optimal Nonmyopic Contact Center Planning Using Dual Decomposition Akshat Kumar and Sudhanshu Singh and Pranav Gupta and Gyana Parija IBM India Research Lab akshat.kumar@gmail.com, {sudsing3,prguptan,gyana.parija}@in.ibm.com
More informationModeling and Simulation of a Pacing Engine for Proactive Campaigns in Contact Center Environment
Modeling and Simulation of a Pacing Engine for Proactive Campaigns in Contact Center Environment Nikolay Korolev, Herbert Ristock, Nikolay Anisimov Genesys Telecommunications Laboratories (an Alcatel-Lucent
More informationNearest Neighbour Algorithms for Forecasting Call Arrivals in Call Centers
Nearest Neighbour Algorithms for Forecasting Call Arrivals in Call Centers Sandjai Bhulai, Wing Hong Kan, and Elena Marchiori Vrije Universiteit Amsterdam Faculty of Sciences De Boelelaan 1081a 1081 HV
More informationWorkforce Scheduling in Inbound Customer Call Centres With a Case Study
Workforce Scheduling in Inbound Customer Call Centres With a Case Study Author(s) anonymised ABSTRACT Call centres are an important tool businesses use to interact with their customers. Their efficiency
More informationCall Centers. November 1999
Call Centers Version 2.1a Thomas A. Grossman, Jr., University of Calgary, Canada* Douglas A. Samuelson, InfoLogix, Inc., Annandale, Virginia Sherry L. Oh & Thomas R. Rohleder, University of Calgary, Canada
More informationProfit-oriented shift scheduling of inbound contact centers with skills-based routing, impatient customers, and retrials
Noname manuscript No. (will be inserted by the editor) Profit-oriented shift scheduling of inbound contact centers with skills-based routing, impatient customers, and retrials Stefan Helber and Kirsten
More informationProfit Maximization and Power Management of Green Data Centers Supporting Multiple SLAs
Profit Maximization and Power Management of Green Data Centers Supporting Multiple SLAs Mahdi Ghamkhari and Hamed Mohsenian-Rad Department of Electrical Engineering University of California at Riverside,
More informationAS-D2 THE ROLE OF SIMULATION IN CALL CENTER MANAGEMENT. Dr. Roger Klungle Manager, Business Operations Analysis
AS-D2 THE ROLE OF SIMULATION IN CALL CENTER MANAGEMENT Dr. Roger Klungle Manager, Business Operations Analysis AAA Michigan 1 Auto Club Drive Dearborn, MI 48126 U.S.A. Phone: (313) 336-9946 Fax: (313)
More informationVEHICLE ROUTING PROBLEM
VEHICLE ROUTING PROBLEM Readings: E&M 0 Topics: versus TSP Solution methods Decision support systems for Relationship between TSP and Vehicle routing problem () is similar to the Traveling salesman problem
More informationSimulation of Call Center With.
Chapter 4 4.1 INTRODUCTION A call center is a facility designed to support the delivery of some interactive service via telephone communications; typically an office space with multiple workstations manned
More informationStaffing Call Centers with Uncertain Arrival Rates and Co-sourcing
Staffing Call Centers with Uncertain Arrival Rates and Co-sourcing Yaşar Levent Koçağa Syms School of Business, Yeshiva University, Belfer Hall 403/A, New York, NY 10033, kocaga@yu.edu Mor Armony Stern
More informationUsing Simulation to Understand and Optimize a Lean Service Process
Using Simulation to Understand and Optimize a Lean Service Process Kumar Venkat Surya Technologies, Inc. 4888 NW Bethany Blvd., Suite K5, #191 Portland, OR 97229 kvenkat@suryatech.com Wayne W. Wakeland
More informationOptimizing Call Center Staffing using Simulation and Analytic Center Cutting Plane Methods
Submitted to Management Science manuscript MS-00998-2004.R1 Optimizing Call Center Staffing using Simulation and Analytic Center Cutting Plane Methods Júlíus Atlason, Marina A. Epelman Department of Industrial
More informationRESOURCE POOLING AND STAFFING IN CALL CENTERS WITH SKILL-BASED ROUTING
RESOURCE POOLING AND STAFFING IN CALL CENTERS WITH SKILL-BASED ROUTING by Rodney B. Wallace IBM and The George Washington University rodney.wallace@us.ibm.com Ward Whitt Columbia University ward.whitt@columbia.edu
More informationTEACHING AGGREGATE PLANNING IN AN OPERATIONS MANAGEMENT COURSE
TEACHING AGGREGATE PLANNING IN AN OPERATIONS MANAGEMENT COURSE Johnny C. Ho, Turner College of Business, Columbus State University, Columbus, GA 31907 David Ang, School of Business, Auburn University Montgomery,
More informationA Software Development Simulation Model of a Spiral Process
A Software Development Simulation Model of a Spiral Process ABSTRACT: There is a need for simulation models of software development processes other than the waterfall because processes such as spiral development
More informationA joint chance-constrained programming approach for call center workforce scheduling under uncertain call arrival forecasts
*Manuscript Click here to view linked References A joint chance-constrained programming approach for call center workforce scheduling under uncertain call arrival forecasts Abstract We consider a workforce
More informationOptimal shift scheduling with a global service level constraint
Optimal shift scheduling with a global service level constraint Ger Koole & Erik van der Sluis Vrije Universiteit Division of Mathematics and Computer Science De Boelelaan 1081a, 1081 HV Amsterdam The
More informationRule-based Traffic Management for Inbound Call Centers
Vrije Universiteit Amsterdam Research Paper Business Analytics Rule-based Traffic Management for Inbound Call Centers Auteur: Tim Steinkuhler Supervisor: Prof. Dr. Ger Koole October 7, 2014 Contents Preface
More informationABSTRACT WITH TIME-VARYING ARRIVALS. Ahmad Ridley, Doctor of Philosophy, 2004
ABSTRACT Title of Dissertation: PERFORMANCE ANALYSIS OF A MULTI-CLASS, PREEMPTIVE PRIORITY CALL CENTER WITH TIME-VARYING ARRIVALS Ahmad Ridley, Doctor of Philosophy, 2004 Dissertation directed by: Professor
More informationMotivated by a problem faced by a large manufacturer of a consumer product, we
A Coordinated Production Planning Model with Capacity Expansion and Inventory Management Sampath Rajagopalan Jayashankar M. Swaminathan Marshall School of Business, University of Southern California, Los
More informationCreating operational shift schedules for third-level IT support: challenges, models and case study
242 Int. J. Services Operations and Informatics, Vol. 3, Nos. 3/4, 2008 Creating operational shift schedules for third-level IT support: challenges, models and case study Segev Wasserkrug*, Shai Taub,
More informationCHAPTER 1 INTRODUCTION
CHAPTER 1 INTRODUCTION Power systems form the largest man made complex system. It basically consists of generating sources, transmission network and distribution centers. Secure and economic operation
More informationA MANAGER S ROADMAP GUIDE FOR LATERAL TRANS-SHIPMENT IN SUPPLY CHAIN INVENTORY MANAGEMENT
A MANAGER S ROADMAP GUIDE FOR LATERAL TRANS-SHIPMENT IN SUPPLY CHAIN INVENTORY MANAGEMENT By implementing the proposed five decision rules for lateral trans-shipment decision support, professional inventory
More informationParametric Stochastic Programming Models for Call-Center Workforce Scheduling
Parametric Stochastic Programming Models for Call-Center Workforce Scheduling Noah Gans Haipeng Shen Yong-Pin Zhou OPIM Department Department of Statistics and OR ISOM Department The Wharton School UNC
More informationIntelligent Search Heuristics for Cost Based Scheduling. Murphy Choy Michelle Cheong. Abstract
Intelligent Search Heuristics for Cost Based Scheduling Murphy Choy Michelle Cheong Abstract Nurse scheduling is a difficult optimization problem with multiple constraints. There is extensive research
More informationA Study of Local Optima in the Biobjective Travelling Salesman Problem
A Study of Local Optima in the Biobjective Travelling Salesman Problem Luis Paquete, Marco Chiarandini and Thomas Stützle FG Intellektik, Technische Universität Darmstadt, Alexanderstr. 10, Darmstadt,
More informationModeling Multi-Echelon Multi-Supplier Repairable Inventory Systems with Backorders
J. Service Science & Management, 2010, 3, 440-448 doi:10.4236/jssm.2010.34050 Published Online December 2010 (http://www.scirp.org/journal/jssm) Modeling Multi-Echelon Multi-Supplier Repairable Inventory
More informationPrinciples of demand management Airline yield management Determining the booking limits. » A simple problem» Stochastic gradients for general problems
Demand Management Principles of demand management Airline yield management Determining the booking limits» A simple problem» Stochastic gradients for general problems Principles of demand management Issues:»
More informationTIME DEPENDENT PRIORITIES IN CALL CENTERS
TIME DEPENDENT PRIORITIES IN CALL CENTERS LASSAAD ESSAFI and GUNTER BOLCH Institute of Computer Science University of Erlangen-Nuremberg Martensstrasse 3, D-91058 Erlangen, Germany E-mail : lassaad@essafi.de,
More informationA TUTORIAL ON MODELLING CALL CENTRES USING DISCRETE EVENT SIMULATION
A TUTORIAL ON MODELLING CALL CENTRES USING DISCRETE EVENT SIMULATION Benny Mathew Manoj K. Nambiar Innovation Lab Performance Engineering Innovation Lab Performance Engineering Tata Consultancy Services
More informationMeta-Heuristics for Reconstructing Cross Cut Shredded Text Documents
Meta-Heuristics for Reconstructing Cross Cut Shredded Text Documents Matthias Prandtstetter Günther R. Raidl Institute of Computer Graphics and Algorithms Vienna University of Technology, Austria www.ads.tuwien.ac.at
More informationFluid Approximation of a Priority Call Center With Time-Varying Arrivals
Fluid Approximation of a Priority Call Center With Time-Varying Arrivals Ahmad D. Ridley, Ph.D. William Massey, Ph.D. Michael Fu, Ph.D. In this paper, we model a call center as a preemptive-resume priority
More informationSimulation of a Claims Call Center: A Success and a Failure
Proceedings of the 1999 Winter Simulation Conference P. A. Farrington, H. B. Nembhard, D. T. Sturrock, and G. W. Evans, eds. SIMULATION OF A CLAIMS CALL CENTER: A SUCCESS AND A FAILURE Roger Klungle AAA
More informationA Comparison of General Approaches to Multiprocessor Scheduling
A Comparison of General Approaches to Multiprocessor Scheduling Jing-Chiou Liou AT&T Laboratories Middletown, NJ 0778, USA jing@jolt.mt.att.com Michael A. Palis Department of Computer Science Rutgers University
More informationWeb Hosting Service Level Agreements
Chapter 5 Web Hosting Service Level Agreements Alan King (Mentor) 1, Mehmet Begen, Monica Cojocaru 3, Ellen Fowler, Yashar Ganjali 4, Judy Lai 5, Taejin Lee 6, Carmeliza Navasca 7, Daniel Ryan Report prepared
More informationProceedings of the 2008 Winter Simulation Conference S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds.
Proceedings of the 2008 Winter Simulation Conference S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. SPEEDING UP CALL CENTER SIMULATION AND OPTIMIZATION BY MARKOV CHAIN UNIFORMIZATION
More informationSIMULATION FOR IT SERVICE DESK IMPROVEMENT
QUALITY INNOVATION PROSPERITY/KVALITA INOVÁCIA PROSPERITA XVIII/1 2014 47 SIMULATION FOR IT SERVICE DESK IMPROVEMENT DOI: 10.12776/QIP.V18I1.343 PETER BOBER Received 7 April 2014, Revised 30 June 2014,
More informationConclusions and Suggestions for Future Research
6 Conclusions and Suggestions for Future Research In this thesis dynamic inbound contact centers with heterogeneous agents and retrials of impatient customers were analysed. The term dynamic characterises
More informationA Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data
A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data Athanasius Zakhary, Neamat El Gayar Faculty of Computers and Information Cairo University, Giza, Egypt
More informationAnalysis of Load Frequency Control Performance Assessment Criteria
520 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 16, NO. 3, AUGUST 2001 Analysis of Load Frequency Control Performance Assessment Criteria George Gross, Fellow, IEEE and Jeong Woo Lee Abstract This paper presents
More informationUseful Recent Trends in Simulation Methodology
Useful Recent Trends in Simulation Methodology Shane Henderson Cornell University Thanks: NSF DMI 0400287 1 e.g., Inbound call centre staffing Min staffing costs s/t customers happy enough Min c T x s/t
More informationProject management: a simulation-based optimization method for dynamic time-cost tradeoff decisions
Rochester Institute of Technology RIT Scholar Works Theses Thesis/Dissertation Collections 2009 Project management: a simulation-based optimization method for dynamic time-cost tradeoff decisions Radhamés
More informationA Brief Study of the Nurse Scheduling Problem (NSP)
A Brief Study of the Nurse Scheduling Problem (NSP) Lizzy Augustine, Morgan Faer, Andreas Kavountzis, Reema Patel Submitted Tuesday December 15, 2009 0. Introduction and Background Our interest in the
More informationDIRECT MAIL: MEASURING VOLATILITY WITH CRYSTAL BALL
Proceedings of the 2005 Crystal Ball User Conference DIRECT MAIL: MEASURING VOLATILITY WITH CRYSTAL BALL Sourabh Tambe Honza Vitazka Tim Sweeney Alliance Data Systems, 800 Tech Center Drive Gahanna, OH
More informationHow To Manage A Call Center
THE ROLE OF SIMULATION IN CALL CENTER MANAGEMENT Roger Klungle AAA Michigan Introduction With recent advances in technology and the changing nature of business, call center management has become a rapidly
More informationMSD Supply Chain Programme Strategy Workshop
MSD Supply Chain Programme Strategy Workshop Day 2 APPENDIX Accenture Development Partnerships Benchmarking MSD s Current Operating Supply Chain Capability 1.0 Planning 2.0 Procurement 3.0 Delivery 4.0
More informationOPTIMUM TOUR SCHEDULING OF IT HELP DESK AGENTS
OPTIMUM TOUR SCHEDULING OF IT HELP DESK AGENTS Hesham K. Alfares Systems Engineering Department College of Computer Sciences and Engineering King Fahd University of Petroleum & Minerals Saudi Arabia hesham@ccse.kfupm.edu.sa
More informationCall centers are an increasingly important part of today s business world, employing millions of
PRODUCTION AND OPERATIONS MANAGEMENT Vol. 16, No. 6, November-December 2007, pp. 665 688 issn 1059-1478 07 1606 665$1.25 POMS doi 10.3401/poms. 2007 Production and Operations Management Society The Modern
More informationCAPACITY PLANNING IN A VIRTUAL CALL CENTER USING AUCTIONS TO PERFORM ROSTERING
CAPACITY PLANNING IN A VIRTUAL CALL CENTER USING AUCTIONS TO PERFORM ROSTERING Matthew F. Keblis College of Business University of Dallas Irving, TX 75062 Phone: (972) 265-5719 Fax: (972) 265-5750 Email:
More informationDark Fiber Lease Considerations
This memorandum presents a brief framework for pricing and marketing of dark fiber. With the obvious caveat that a detailed analysis requires an in-depth evaluation of a given project or market, this general
More informationA Programme Implementation of Several Inventory Control Algorithms
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume, No Sofia 20 A Programme Implementation of Several Inventory Control Algorithms Vladimir Monov, Tasho Tashev Institute of Information
More informationSTOCHASTIC SERVICE NETWORK DESIGN: THE IMPORTANCE OF TAKING UNCERTAINTY INTO ACCOUNT
Advanced OR and AI Methods in Transportation STOCHASTIC SERVICE NETWORK DESIGN: THE IMPORTANCE OF TAKING UNCERTAINTY INTO ACCOUNT Arnt-Gunnar LIUM, Stein W. WALLACE, Teodor Gabriel CRAINIC Abstract. The
More informationLECTURE - 3 RESOURCE AND WORKFORCE SCHEDULING IN SERVICES
LECTURE - 3 RESOURCE AND WORKFORCE SCHEDULING IN SERVICES Learning objective To explain various work shift scheduling methods for service sector. 8.9 Workforce Management Workforce management deals in
More informationPeriodic Capacity Management under a Lead Time Performance Constraint
Periodic Capacity Management under a Lead Time Performance Constraint N.C. Buyukkaramikli 1,2 J.W.M. Bertrand 1 H.P.G. van Ooijen 1 1- TU/e IE&IS 2- EURANDOM INTRODUCTION Using Lead time to attract customers
More informationA Practical Guide to Seasonal Staffing Alternatives
A Practical Guide to Seasonal Staffing Alternatives It s summer and time to plan for your peak staffing needs for the busy holiday season. While your base staff will carry you throughout the summer, you
More informationFlexible Distributed Capacity Allocation and Load Redirect Algorithms for Cloud Systems
Flexible Distributed Capacity Allocation and Load Redirect Algorithms for Cloud Systems Danilo Ardagna 1, Sara Casolari 2, Barbara Panicucci 1 1 Politecnico di Milano,, Italy 2 Universita` di Modena e
More informationNurse Rostering. Jonathan Johannsen CS 537. Scheduling Algorithms
Nurse Rostering Jonathan Johannsen CS 537 Scheduling Algorithms Most hospitals worldwide create schedules for their staff by hand, spending hours trying to optimally assign workers to various wards at
More informationKnowledge Management in Call Centers: How Routing Rules Influence Expertise and Service Quality
Knowledge Management in Call Centers: How Routing Rules Influence Expertise and Service Quality Christoph Heitz Institute of Data Analysis and Process Design, Zurich University of Applied Sciences CH-84
More informationSYNERGISTIC MODELING OF CALL CENTER OPERATIONS
SYNERGISTIC MODELING OF CALL CENTER OPERATIONS DENNIS C. DIETZ AND JON G. VAVER Received 14 July 2005; Revised 30 November 2005; Accepted 9 December 2005 We synergistically apply queueing theory, integer
More informationAachen Summer Simulation Seminar 2014
Aachen Summer Simulation Seminar 2014 Lecture 07 Input Modelling + Experimentation + Output Analysis Peer-Olaf Siebers pos@cs.nott.ac.uk Motivation 1. Input modelling Improve the understanding about how
More informationStaffing Call Centers with Uncertain Arrival Rates and Co-sourcing
Staffing Call Centers with Uncertain Arrival Rates and Co-sourcing Yaşar Levent Koçağa Sy Syms School of Business, Yeshiva University, Belfer Hall 43/A, New York, NY 133, kocaga@yu.edu Mor Armony Stern
More informationA MANAGER-FRIENDLY PLATFORM FOR SIMULATION MODELING AND ANALYSIS OF CALL CENTER QUEUEING SYSTEMS. Robert Saltzman Vijay Mehrotra
Proceedings of the 2004 Winter Simulation Conference R.G. Ingalls, M. D. Rossetti, J. S. Smith, and B. A. Peters, eds. A MANAGER-FRIENDLY PLATFORM FOR SIMULATION MODELING AND ANALYSIS OF CALL CENTER QUEUEING
More informationMeasuring and Interpreting the Performance of Broker Algorithms
Measuring and Interpreting the Performance of Broker Algorithms Ian Domowitz Managing Director ITG Inc. 380 Madison Ave. New York, NY 10017 212 588 4000 idomowitz@itginc.com Henry Yegerman Director ITG
More informationRoulette Wheel Testing. Report on Stage 3.1 of NWML/GBGB Project Proposal
NOTICE: Following large wins from professional roulette teams, the UK Weights and Measures Lab (government lab) conducted a study to determine if particular "wheel conditions" made roulette spins predictable.
More informationHow To Model A System
Web Applications Engineering: Performance Analysis: Operational Laws Service Oriented Computing Group, CSE, UNSW Week 11 Material in these Lecture Notes is derived from: Performance by Design: Computer
More informationContact Center Planning Calculations and Methodologies
Contact Center Planning Calculations and Methodologies A Comparison of Erlang-C and Simulation Modeling Ric Kosiba, Ph.D. Vice President Interactive Intelligence, Inc. Bayu Wicaksono Manager, Operations
More informationBusiness analytics for flexible resource allocation under random emergencies
Submitted to Management Science manuscript Authors are encouraged to submit new papers to INFORMS journals by means of a style file template, which includes the journal title. However, use of a template
More informationOptimizing Call Center Staffing using Simulation and Analytic Center Cutting Plane Methods
Optimizing Call Center Staffing using Simulation and Analytic Center Cutting Plane Methods Júlíus Atlason, jatlason@umich.edu Marina A. Epelman, mepelman@umich.edu Shane G. Henderson, sgh9@cornell.edu
More informationDistributionally robust workforce scheduling in call centers with uncertain arrival rates
Distributionally robust workforce scheduling in call centers with uncertain arrival rates S. Liao, C. van Delft, J.-P. Vial May 2011 Abstract Call center scheduling aims to set-up the workforce so as to
More informationEmpirically Identifying the Best Genetic Algorithm for Covering Array Generation
Empirically Identifying the Best Genetic Algorithm for Covering Array Generation Liang Yalan 1, Changhai Nie 1, Jonathan M. Kauffman 2, Gregory M. Kapfhammer 2, Hareton Leung 3 1 Department of Computer
More information